Senior/Staff Software Engineer [Full Stack]

Prudentia Sciences•Boston, NY

6d•Remote

About The Position

We're seeking an exceptional Senior/Staff Software Engineer to build and lead our core platform as we scale. This is a rare opportunity to join a high-impact technology company in a major growth stage, where you'll act as tech lead for our core platform, ensuring our pharma and biopharma customers achieve transformative outcomes with our platform. Our platform is quickly seeing exceptional demand in its sector, and backed by top tier life science investors who have an informed perspective on where AI can have a profound impact in the industry.

Requirements

Bachelor’s, Master’s, or Ph.D. in Computer Science, Software Engineering, or a related technical field.
Proven experience building modern web applications end-to-end — from intuitive, performant front-ends (React, Next.js, or similar) to robust, scalable back-ends (FastAPI, Node.js, or equivalent).
Hands-on experience designing and implementing complex, data-driven applications that integrate with APIs, asynchronous job systems, or machine learning backends.
Strong command of component-based design, state management, and visualization frameworks (e.g., React Query, Redux, D3, Plotly) to deliver interactive, insight-driven user experiences.
Expertise in developing RESTful or GraphQL APIs, integrating authentication/authorization, and managing event-driven workflows and background jobs.
Comfort working with both relational and NoSQL databases (e.g., Postgres, MongoDB, DynamoDB), and designing efficient data access layers for large, dynamic datasets.
Experience deploying full-stack applications in cloud environments (AWS, GCP, or Azure) using modern DevOps practices — including Docker, Kubernetes, Terraform, and CI/CD pipelines.
Familiarity with best practices for secure data handling, user authentication, and compliance (especially valuable in healthcare, life sciences, or enterprise environments).
Strong communication and collaboration skills; ability to work closely with ML engineers, product managers, and scientific domain experts to deliver elegant, high-impact user workflows.
Strong problem-solving skills and an analytical mindset.
Passion for continuous learning, rapid prototyping, and iterating based on user needs.
Autonomous, self-starter attitude with a strong sense of ownership.
Excellent communication skills—able to explain technical ideas clearly to non-technical audiences.
Collaborative team player with a desire to build things that truly matter.

Nice To Haves

Experience in healthcare, life sciences, or biopharma sectors is nice, but not required. More important is a willingness to dive into the field and a curiosity to learn about life sciences and the drug development process, and how deal making revolves around it.

Responsibilities

End-to-End Platform Ownership: Design, build, and scale the web platform that enables deal teams to explore, upload, and analyze drug assets from discovery through due diligence and valuation.
Front-End Architecture & UX: Develop intuitive, data-rich interfaces using modern frameworks (React/Next.js preferred) that empower users to manage deal pipelines, upload documents, and interpret LLM-driven insights.
Workflow & Orchestration: Implement robust backend services and job orchestration layers (e.g., FastAPI, Node, or similar) that coordinate document ingestion, model execution, and results delivery across the platform.
Data Visualization & Insight Delivery: Create dynamic, interactive components that visualize scientific assessments, risk analyses and deal insights generated by ML pipelines.
API & Integration Engineering: Design and maintain clean, scalable APIs between the core LLM orchestration layer and the platform. Collaborate closely with ML engineers to expose model outputs as user-ready insights.
Reliability & Scalability: Deploy and monitor platform services on AWS (or equivalent). Ensure high availability, low latency, and secure handling of sensitive scientific and deal data.
Collaboration & Product Thinking: Work cross-functionally with ML engineers, product leads, and domain experts to translate scientific and business logic into actionable workflows that drive decision-making.
Continuous Improvement: Champion engineering best practices — automated testing, CI/CD, observability, and modular architecture — while staying current on advances in AI-driven platform development.